Project Summary
The Athena project will develop technology called "search as
learning," a set of search technologies that encourage and support
learning rather than just simple document finding. In order to learn,
searchers must engage with information that is both novel and
understandable. Therefore, at the core, Athena will support learning by
modeling several important factors: (1) the knowledge connections between
documents covering a topic, (2) a user's current state of knowledge on
that topic, (3) the types of knowledge a user is likely to gain from a
document, and (4) the knowledge required for a user to successfully
engage with a document. The Athena project will involve two types of
end-to-end systems, both of which will model and leverage the learner's
state of knowledge (LSK): an LSK-aware search engine and an LSK-aware
question answering system. The Athena systems will guide a user through a
topic and find relevant information in the context of previously
encountered information and the topic structure captured in a web of
topics. The team will evaluate Athena using standard measures as well as
a series of studies involving human subjects. If the Athena project is
successful, it will make it easier for people to use search engines and
related technologies to learn about complex topics, where there are
numerous interrelated and dependent subtopics that should be considered.
Given that search is among the most common online activities on and off
the Web, Athena and its technologies will have a substantial impact on
searchers trying to learn about topics.
Athena enables "search as learning" using a data structure referred to
as a Learning Flow Graph (LFG). An LFG comprises nodes that represent
sub-topics (e.g., concepts) within a given domain and vertices that
represent relations between sub-topics (e.g., one sub-topic being
foundational to understand another). Athena leverages LFGs to model the
different factors mentioned above. It uses probability distributions
across nodes in an LFG to model: (1) a user's knowledge state, (2) the
potential knowledge gains from an information item, and (3) the
prerequisite knowledge required for a user to successfully engage with an
information item. The Athena team will develop algorithms for generating
LFGs from structured and semi- and unstructured resources (e.g., course
syllabi, tables of contents, book indices, knowledge bases, query logs),
algorithms for integrating LFGs into search and question-answering
models, and algorithms for re-estimating LFGs and a user's knowledge
state based on search behaviors (e.g., queries, clicks, skips, dwell
times, etc.).
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